Artificial neural networks in prediction of antifungal activity of a series of pyridine derivatives against Candida albicans

Journal of Microbiological Methods

Volumn 76, Issue 1, 2009, Pages 25-29

  Buciński, Adam ^a   Socha, Agnieszka ^a   Wnuk, Małgorzata ^a   Baczek, Tomasz ^a,b   Nowaczyk, Alicja ^a   Krysiński, Jerzy ^a   Goryński, Krzysztof ^a   Koba, Marcin ^a  

^a NICOLAUS COPERNICUS UNIVERSITY   (Poland)

^b MEDICAL UNIVERSITY OF GDANSK   (Poland)

Author keywords

Antifungal activity; Artificial neural networks (ANNs); Molecular descriptors; Pyridine derivatives; Sensitivity analysis

Indexed keywords

PYRIDINE DERIVATIVE;

ANTIFUNGAL ACTIVITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACTERIAL STRAIN; CANDIDA ALBICANS; CONTROLLED STUDY; DRUG POTENCY; DRUG STRUCTURE; MINIMUM INHIBITORY CONCENTRATION; NONHUMAN; PRIORITY JOURNAL; STRUCTURE ACTIVITY RELATION;

ANTIFUNGAL AGENTS; CANDIDA ALBICANS; CANDIDIASIS; HUMANS; NEURAL NETWORKS (COMPUTER); PYRIDINES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

CANDIDA ALBICANS;

EID: 57149147407     PISSN: 01677012     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mimet.2008.09.003     Document Type: Article

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